That’s a Wrap on CoreShift 2026
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Accelerating the NEXT Shift in Enterprise Data & AI
The live event has concluded, but the conversations continue. Thank you for being part of CoreShift 2026. Revisit all eight sessions on demand and catch the insights, demos, and perspectives you may have missed.
Who Attended CoreShift 2026
CoreShift 2026 brought together enterprise leaders, customers, partners, and technology experts to explore how organizations are moving Data & AI from experimentation to enterprise scale.
Watch all eight sessions on demand for practical insights, real-world use cases, live demos, and proven approaches across Data, AI, governance, real-time intelligence, ontologies, digital twins, and industry transformation.
CoreShift 2026: On-Demand Technology Track
From AI Pilots to Enterprise-Scale Agentic AI
Moving an AI agent from a successful pilot into production changes the questions enterprises need to answer. Accuracy alone is not enough — organizations need repeatable evaluation, release discipline, predictable economics, performance at scale, governance, and visibility into real-world outcomes.
This session explores a practical path for taking enterprise agents from pilot to production, including what changes when real users, business systems, budgets, security requirements, and operational ownership enter the picture.
Key Takeaways
- Build core, edge, and deny evaluations into the development lifecycle instead of waiting until production to discover failures.
- Treat production deployment as a managed release system with environments, testing, versioning, automation, and rollback.
- Understand cost and latency per successful business outcome, not simply per user or interaction.
- Design for multiple specialist agents and shared enterprise context as scope expands.
- Connect observability to actual outcomes — including adoption, failures, tool calls, cost, latency, hand-offs, and business value.
Ontologies & Semantic Context: Building Shared Business Context for AI
Enterprise data may be abundant, but business meaning is often fragmented across applications, departments, documents, KPIs, and people.
This session explores how enterprise ontologies create a shared business context by connecting data, assets, processes, entities, relationships, rules, and metrics — giving both people and AI a consistent language for understanding how the business actually works.
Key Takeaways
- Build a shared semantic foundation across enterprise data, models, systems, and business concepts.
- Give business users access to information using business language rather than system-specific terminology.
- Enable context-rich analytics, conversational experiences, planning, and what-if analysis across previously disconnected information.
- Use Microsoft Fabric IQ and related intelligence layers to connect operational data, organizational knowledge, workflows, and external intelligence.
- Start with a focused ontology use case and create a reusable enterprise foundation that can expand over time.
Real-Time Data & OT: Connecting Operational Technology with Enterprise Data & AI
Industrial organizations generate enormous volumes of operational information across sensors, historians, SCADA, DCS, machines, applications, and enterprise systems. The challenge is turning that data into trusted, contextualized intelligence while it can still influence an operational decision.
This session explores how real-time OT data can flow from the edge into enterprise data platforms, analytics, digital twins, dashboards, and intelligent applications using Microsoft Fabric Real-Time Intelligence, Azure IoT Operations, and an integrated industrial data architecture.
Key Takeaways
- Connect OT, historian, sensor, enterprise, third-party, and unstructured data through a scalable industrial data architecture.
- Understand where Microsoft Fabric Real-Time Intelligence and Azure IoT Operations fit across cloud and edge scenarios.
- Process high-speed operational data close to the source when near-real-time decisions are required.
- Contextualize plant and asset data before exposing it to analytics, digital twins, copilots, and other applications.
- Establish the data quality, security, governance, and architecture needed to scale real-time intelligence across multiple plants.
Data & AI Governance: Building Trusted Foundations for Enterprise AI
As organizations expand their use of data, AI, and autonomous agents, governance can no longer operate as a disconnected compliance exercise.
This session examines how enterprises can bring together data governance, AI governance, security, accountability, policy, access controls, auditability, and operational oversight — creating trusted foundations that support innovation without losing control.
Key Takeaways
- Establish clear ownership, authorization, traceability, and accountability for enterprise AI and agents.
- Create governance controls around data access, security, human oversight, policies, audit trails, and lifecycle management.
- Understand a practical five-level maturity model for agent governance, from ad hoc governance through governance-as-code.
- Treat governance as an operating model, not simply a set of policies written after deployment.
- Build data and AI initiatives on foundations that can remain secure, explainable, traceable, and scalable as adoption increases.
CoreShift 2026: On-Demand Industry Track
Industrial AI for Smarter Manufacturing Operations
Manufacturing transformation requires more than deploying individual AI use cases. Data from plants, assets, enterprise applications, engineering systems, sensors, and external sources needs to work together within a common operational context.
This session explores an end-to-end approach to Industrial AI — connecting plant and enterprise data with machine learning, machine vision, ontologies, digital twins, real-time intelligence, and intelligent applications to improve manufacturing and asset performance.
Key Takeaways
- Bring together plant, asset, engineering, enterprise, logistics, and external data into a common Data & AI foundation.
- Use Industrial AI for scenarios such as OEE optimization, predictive maintenance, machine vision, operational decision support, and asset performance.
- Create trusted asset context using enterprise Tag-ID registries, engineering information, and digitized P&IDs.
- Use ontologies to let business users explore manufacturing performance and ask cross-functional questions using familiar business language.
- See how a real enterprise architecture can connect Fabric, data platforms, AI models, copilots, digital twins, and operational systems.
Forward-Deployed Engineering: The Operating Model for Enterprise Data & AI
Many transformation programs slow down between identifying a valuable use case and putting something usable into production.
Forward-Deployed Engineering takes a different approach: small, outcome-focused teams work directly with business users, data, systems, security teams, and real workflows — combining engineering, product thinking, and domain understanding to rapidly move from problem to working capability.
Key Takeaways
- Focus the engagement around one high-value workflow and a measurable business outcome, rather than starting with technology.
- Combine engineering, solution architecture, product management, workflow understanding, governance, and direct user iteration.
- Reuse proven integration, data, ontology, security, evaluation, AgentOps, and adoption patterns instead of starting every implementation from scratch.
- Run security, compliance, data access, and user adoption in parallel with development, rather than treating them as end-stage activities.
- Move from a working vertical slice toward a production roadmap with defined architecture, security, KPIs, support, and ownership.
Connected Digital Twins for Intelligent Industrial Operations
A useful digital twin is more than a 3D model. It becomes significantly more valuable when engineering information, asset context, operational data, enterprise systems, documents, and real-time sensor information are connected around the same physical operation.
This session explores connected digital twins for oil & gas, energy, utilities, and industrial environments, including how organizations can bring together engineering and operational information to improve asset visibility, maintenance, analysis, and decision-making.
Key Takeaways
- Connect engineering information, OT data, enterprise data, documents, and asset context around a common digital representation.
- Build a trusted enterprise asset and Tag-ID foundation rather than relying on disconnected engineering files.
- Combine digital twins with real-time data and Industrial AI to support predictive maintenance and asset performance.
- Make asset information easier to access through dashboards, copilots, intelligent applications, and business-language queries.
- Start with high-value plant or asset problems and expand the twin incrementally rather than attempting to model everything at once.
Executive Roundtable: Scaling Enterprise AI — Customer Perspectives & Lessons Learned
Scaling enterprise transformation is rarely just a technology problem.
In this executive discussion, leaders from across enterprise technology, manufacturing, transformation, and Data & AI share practical perspectives on what changes when organizations move beyond isolated pilots — from ownership and operating models to governance, organizational readiness, integration, adoption, and measurable business outcomes.
Key Takeaways
- Establish business ownership and measurable outcomes before automating complex processes.
- Look at the technology and data capabilities the organization already owns before adding another platform.
- Give intelligent systems governed access to enterprise data and processes instead of creating unnecessary copies and silos.
- Prioritize the workflows carrying the greatest business friction and redesign them around the outcome.
- Recognize that enterprise-scale AI transformation is as much an operating-model and organizational change as it is a technology transformation.
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